A primary objective of the proposed center is to establish a laboratory for ongoing optimization of a comprehensive smoking cessation intervention. Our definition of intervention optimization is: "Employing scientific theory and efficient methodology to produce an evidence-based intervention that represents the maximal improvement in effectiveness, translatability and/or cost-effectiveness over currently available interventions." In the proposed center we plan to approach intervention optimization by using an innovative optimization cycle framework inspired by engineering methods. The optimization cycle is an extension and elaboration of the Multiphase Optimization Strategy (MOST), a phased experimental approach to intervention development and optimization developed by Collins, Murphy, Nair, and Strecher1 2. Potential advantages of using the, optimization cycle framework include: It is a systematic and principled approach, and therefore is the fastest way, in the long run, to an appreciably improved smoking cessation intervention;it provides a straightforward way of testing hypotheses about potential new intervention components or changes to existing components;it provides a natural way of including cost information in the basis for decision making about intervention development;and it will help contribute to the building of a coherent cumulative base of scientific knowledge about smoking cessation. The Intervention Optimization Core has four Specific Aims: (1) to serve as an intellectual resource to the center, providing ongoing input on the innovative optimization cycle framework and related statistical and methodological consultation as needed;(2) to conduct original methodological work to refine the optimization cycle approach, based on issues that arise in the course of the proposed optimization cycle;(3) to disseminate information about the optimization cycle approach to drug abuse prevention and treatment scientists, using the work of the proposed center as an illustration;and (4) to conduct exploratory analyses in preparation for a second optimization cycle, in collaboration with the Methods Core.
The health, economic, and human costs of tobacco use are profound. The Intervention Optimization Core (IOC) is a critical component of the proposed center that will facilitate use of an innovative, efficient methodology for developing optimal smoking cessation interventions. The IOC will also advance intervention science by further studying this methodology so that it can be used by scientists in other fields.
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|Schlam, Tanya R; Fiore, Michael C; Smith, Stevens S et al. (2016) Comparative effectiveness of intervention components for producing long-term abstinence from smoking: a factorial screening experiment. Addiction 111:142-55|
|Schulte, Danielle M; Duster, Megan; Warrack, Simone et al. (2016) Feasibility and patient satisfaction with smoking cessation interventions for prevention of healthcare-associated infections in inpatients. Subst Abuse Treat Prev Policy 11:15|
|Baker, Timothy B; Collins, Linda M; Mermelstein, Robin et al. (2016) Enhancing the effectiveness of smoking treatment research: conceptual bases and progress. Addiction 111:107-16|
|Cook, Jessica W; Collins, Linda M; Fiore, Michael C et al. (2016) Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: a factorial screening experiment. Addiction 111:117-28|
|Zhang, Xiao; Martinez-Donate, Ana P; Kuo, Daphne et al. (2016) Beyond cigarette smoking: smoke-free home rules and use of alternative tobacco products. Perspect Public Health 136:30-3|
|Yoo, Woohyun; Yang, JungHwan; Cho, Eunji (2016) How social media influence college students' smoking attitudes and intentions. Comput Human Behav 64:173-182|
|Piper, Megan E; Fiore, Michael C; Smith, Stevens S et al. (2016) Identifying effective intervention components for smoking cessation: a factorial screening experiment. Addiction 111:129-41|
|Piper, Megan E; Schlam, Tanya R; Cook, Jessica W et al. (2016) Toward precision smoking cessation treatment I: Moderator results from a factorial experiment. Drug Alcohol Depend 171:59-65|
|Loh, Wei-Yin; He, Xu; Man, Michael (2015) A regression tree approach to identifying subgroups with differential treatmentâ€‰effects. Stat Med 34:1818-33|
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